RL has a rich literature on energy-based, or maximum entropy, methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, which are close or equivalent to the GFlowNet framework in certain settings (in particular when the MDP has a tree structure [7]}). Also related are methods that maximize entropy not on the policy, but rather on the state visitation distribution [8]}, [9]}, [10]} or some proxy of it [11]}, which achieve a similar objective to GFlowNet models by flattening the state visitation distribution.
If the state graph of the environment is a directed tree, the loss \(\mathcal {L}_{\rm SubTB}\)  on individual subtrajectories is equivalent to that of path consistency learning [4]}. However, attempts to use path consistency learning in settings without intermediate rewards have only computed the loss on subtrajectories that have length 1 or include a terminal state [13]}.
